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GCP-DE

Data Engineering on Google Cloud Training

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.
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Course Details

Duration

4 days

Prerequisites

  • Basic proficiency with a common query language such as SQL.
  • Experience with data modeling and ETL (extract, transform, load) activities.
  • Experience with developing applications using a common programming language such as Python.
  • Familiarity with machine learning and/or statistics.

Target Audience

Developers who are responsible for:
  • Extracting, Loading, Transforming, cleaning, and validating data
  • Designing pipelines and architectures for data processing
  • Integrating analytics and machine learning capabilities into data pipelines
  • Querying datasets, visualizing query results and creating reports

Skills Gained

  • Design and build data processing systems on Google Cloud.
  • Process batch and streaming data by implementing autoscaling data pipelines on Dataflow.
  • Derive business insights from extremely large datasets using BigQuery.
  • Leverage unstructured data using Spark and ML APIs on Dataproc.
  • Enable instant insights from streaming data.
  • Understand ML APIs and BigQuery ML, and learn to use AutoML to create powerful models without coding.
Course Outline
  • Introduction to Data Engineering
  • Building a Data Lake
  • Building a Data Warehouse
  • Introduction to Building Batch Data Pipelines
  • Executing Spark on Dataproc
  • Serverless Data Processing with Dataflow
  • Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
  • Introduction to Processing Streaming Data
  • Serverless Messaging with Pub/Sub
  • Dataflow Streaming Features
  • High-Throughput BigQuery and Bigtable Streaming Features
  • Advanced BigQuery Functionality and Performance
  • Introduction to Analytics and AI
  • Prebuilt ML Model APIs for Unstructured Data
  • Big Data Analytics with Notebooks
  • Production ML Pipelines
  • Custom Model Building with SQL in BigQuery ML
  • Custom Model Building with AutoML